Validation step is not happening while training

[shekarneo]

Search before asking

• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Training

Bug

Hi Run the training script with 5000 epochs and save weights at every 50 epochs, but it is not doing validation step and not giving any metric results.

python3 train.py --data ../training/dataset.yaml --weights '' --cfg models/yolov5l.yaml --cache --img 640 --evolve --batch-size 8 --save-period 50 --epochs 5000 --optimizer Adam

its been 12th epochs getting these results:

Epoch gpu_mem box obj cls labels img_size
8/4999 5.04G 0.06438 0.08565 0.01406 51 640: 100%|██████████| 1435/1435 [05:42<00:00, 4.20it/s]

 Epoch   gpu_mem       box       obj       cls    labels  img_size
9/4999     5.04G   0.06397   0.08573   0.01407        42       640: 100%|██████████| 1435/1435 [05:51<00:00,  4.09it/s]                           

 Epoch   gpu_mem       box       obj       cls    labels  img_size

10/4999 5.04G 0.06388 0.08541 0.01401 21 640: 100%|██████████| 1435/1435 [05:48<00:00, 4.12it/s]

 Epoch   gpu_mem       box       obj       cls    labels  img_size

11/4999 5.04G 0.06371 0.08526 0.01384 32 640: 100%|██████████| 1435/1435 [05:49<00:00, 4.11it/s]

Environment

No response

Minimal Reproducible Example

python3 train.py --data ../training/dataset.yaml --weights '' --cfg models/yolov5l.yaml --cache --img 640 --evolve --batch-size 8 --save-period 50 --epochs 5000 --optimizer Adam

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @shekarneo, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.

Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@shekarneo there's no bug, it just seems you don't understand how evolution works. Evolution with --evolve argument does not output any intermediate metrics nor save any models, it outputs a set of optimized hyperparameters after several weeks of training hundreds of models. See Hyperparameter Evolution tutorial for details.

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Roboflow for Datasets, Labeling, and Active Learning  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• Architecture Summary  ⭐ NEW

Good luck 🍀 and let us know if you have any other questions!

[shekarneo]

Thanks @glenn-jocher, how do i monitor precesion and recall.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@shekarneo Precision and recall can be monitored using the integrated W&B logging feature in YOLOv5. W&B automatically logs and visualizes all model metrics including precision and recall. Simply enable the --project and --name arguments to start logging your metrics. For further guidance, please refer to the Weights & Biases Logging tutorial in the YOLOv5 documentation.

If you have any more questions feel free to ask!